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Unsupervised learning for Improved Gamma-Ray Spectrometry in Pixelated Cadmium Zinc Telluride (CZT) Detectors

NUCLEAR science AND ENGINEERING 2025年第Supp 1期199卷 S575-S586页

作者： Aversano, G. Parrilla, H. S. Hellfeld, D. Vavrek, J. R. Lawrence Berkeley Natl Lab 1 Cyclotron RdMailstop 50C3396 Berkeley CA 94720 USA

machine learning has been found to be ubiquitously useful across many industries, presenting an opportunity to improve radiation detection performance using data-driven algorithms. Improved detector resolution can aid in the detection, identification, and quantification of radionuclides. In this work, a novel, data-driven, unsupervised learning approach is developed to improve detector spectral characteristics by learning, and subsequently rejecting, poorly performing regions of the pixelated detector. Feature engineering is used to fit individual characteristic photo peaks to a Doniach lineshape with a linear background model. Then, principal component analysis is used to learn a lower-dimension latent space representation of each photo peak where the pixels are clustered, and subsequently ranked, based on the cluster mean distance to an optimal point. Pixels within the worst cluster(s) are rejected to improve the full-width at half-maximum (FWHM) by 10% to 15% (relative to the bulk detector) at 50% net efficiency when applied to training data obtained from measurements of a 100 mu Ci 154Eu source using a H3D M400i pixelated cadmium zinc telluride *** results compare well with, but do not outperform, a greedy algorithm that accumulates pixels in order of FWHM from lowest to highest used as a benchmark. In the future, this approach can be extended to include the detector energy and angular response. Finally, the model is applied to newly seen natural and enriched uranium spectra relevant for nuclear safeguards applications.

关键词： Radiation detection CZT cadmium zinc telluride machine learning unsupervised learning

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A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data

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international JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024年第11期40卷 e3858页

作者： Csala, Hunor Amili, Omid D'Souza, Roshan M. Arzani, Amirhossein Univ Utah Dept Mech Engn Salt Lake City UT 84112 USA Univ Utah Sci Comp & Imaging Inst Salt Lake City UT USA Univ Toledo Dept Mech Ind & Mfg Engn Toledo OH USA Univ Wisconsin Milwaukee Dept Mech Engn Milwaukee WI USA

Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time-resolved three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI), which enables noninvasive time-dependent velocity measurements within large vessels. However, several limitations hinder the usability of 4D flow MRI and other experimental methods for quantitative hemodynamics analysis. These mainly include measurement noise, corrupt or missing data, low spatiotemporal resolution, and other artifacts. Traditional filtering is routinely applied for denoising experimental blood flow data without any detailed discussion on why it is preferred over other methods. In this study, filtering is compared to different singular value decomposition (SVD)-based machine learning and autoencoder-type deep learning methods for denoising and filling in missing data (imputation). An artificially corrupted and voxelized computational fluid dynamics (CFD) simulation as well as in vitro 4D flow MRI data are used to test the methods. SVD-based algorithms achieve excellent results for the idealized case but severely struggle when applied to in vitro data. The autoencoders are shown to be versatile and applicable to all investigated cases. For denoising, the in vitro 4D flow MRI data, the denoising autoencoder (DAE), and the Noise2Noise (N2N) autoencoder produced better reconstructions than filtering both qualitatively and quantitatively. Deep learning methods such as N2N can result in noise-free velocity fields even though they did not use clean data during training. This work presents one of the first comprehensive assessments and comparisons of various classical and modern machine-learning methods for enhancing corrupt cardiovascular flow data in diseased arteries for both synthetic and experimental test cases.

关键词： data imputation deep learning denoising hemodynamics sparse data-driven modeling

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Development of a machine learning Model for Enhancing the Security of the Internet of Things (IoT) System 5th

Development of a Machine Learning Model for Enhancing the Se...

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5th international conference on data science, machine learning and Applications

作者： Raminenei, Kamalakar Gupta, Vratika Durgam, Thirupathi Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Teerthanker Mahaveer Univ Coll Comp Sci & Informat Technol Moradabad India St Martins Engn Coll Secunderabad ECE Secunderabad Telangana India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In the previous decade, Internet of Things (IoT) systems have grown into a worldwide behemoth that has encompassed every element of everyday existence by enhancing human existence with uncountable intelligent assistance. Due to the ease of usage and increasing need for smart gadgets and networks, IoT is experiencing more security concerns today than ever before. As a result, a powerful constantly improved and current security solution is necessary for contemporary IoT systems. A significant technological improvement in machine learning (ML) has been observed, opening up several potential study avenues for tackling existing and prospective IoT concerns. The fundamental goal of this study is to implement an ML-based model for IoT security enhancement. In the initial phase of this study approach, feature scaled has been performed on the UNSW-NB15 database utilizing the Minimum-maximum idea of normalizing to reduce data leaks on the experimental statistics. Principal Components Assessment (PCA) has been utilized to reduce dimensions in the following phase. Finally, for the investigation, 6 suggested ML solutions have been applied. The outcomes from experiments have been assessed using a validating database. The outcomes have been compared to previous research, and the outcomes have been compatible with an accuracy of 99.99 percent and an MCC-Mathew correlation coefficient of 99.97 percent.

关键词： Internet of things Security machine learning Anomaly detection and Intrusion prevention

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AI Fusion with Adaptive Filter for Non-stationary data in Parameter Prediction 11

AI Fusion with Adaptive Filter for Non-stationary Data in Pa...

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11th IEEE international conference on Consumer Electronics - Taiwan (ICCE-Taiwan) - Empower of Innovative Consumer Technology

作者： Sun, Chun-Hung Su, Yuan-Jun ASE Grp Kaohsiung Smart Test Informat R&D Kaohsiung Taiwan

ISBN: (纸本)9798350386851;9798350386844

In recent years, various fusion technologies have also been used to achieve performance improvement. When AI has been applied widely, one of the major challenges is the resistance of machine learning (ML) models to adapt to non-stationary data streams. Models have been increasingly required to update in a non-stationary environment [1]. In this paper, a fusion regression system is proposed to improve tracking capability for uncertainty and non-stationary data by combining machine learning models and adaptive filters.

关键词： machine learning concept drift adaptive filter fusion

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DEVELOPMENT OF TIME SERIES DRILLING dataSETS FOR STUCK PIPE PREDICTION USING VOLVE FIELD data 43

DEVELOPMENT OF TIME SERIES DRILLING DATASETS FOR STUCK PIPE ...

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ASME 43rd international conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Cao, Bo Song, Yu Xu, Feng Peng, Fukang China Univ Petr Beijing Peoples R China

ISBN: (纸本)9780791887868

Numerous machine learning algorithms are applied in the oil and gas industry. However, the data used in these studies are difficult to obtain due to various limitations. Due to the lack of benchmark datasets, it is challenging to make performance comparisons across different algorithms. Volve field dataset was made public by Equinor, which provides raw data availability for the development of drilling and completion datasets. In this paper, we utilize the time-based drilling data from the Volve drilling platform and transform it into time series datasets for stuck pipe prediction. Specifically, we introduce our concepts and principles for data development, the rules for selecting time intervals and attributes, the challenges encountered during the data development process and the methods for overcoming them. We discuss the applicability of these methods, the issues they bring, and their impact on data quality. Furthermore, we provide a well development case that includes complex data. The results indicated that our research shows promise in providing time series reference datasets for the application of machine learning algorithms in stuck pipe prediction. We aim to provide a reference methodology for the development of raw data, reducing barriers to data utilization. We hope to provide data availability, possibly even serving as a reference benchmark dataset for the further applications of machine learning algorithms in the oil and gas sector. Our datasets are made publicly available on GitHub : https://***/promiseeee/Time-series-stuck-pipeprediction.

关键词： machine learning Drilling data science

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Unlocking the Future: A Decade of Insights into Short-Term Stock Market Trends with machine learning and the S&P 500 Index 4

Unlocking the Future: A Decade of Insights into Short-Term S...

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4th international conference on Innovative Research in applied science, Engineering and Technology, IRASET 2024

作者： Tajbel, Salma Elassad, Zouhair Elamrani Abou Atlas, Abdelghafour Marrakech Morocco Faculty Of Science And Information Technology Data Science Lab Birulia Bangladesh

ISBN: (纸本)9798350309508

This research paper presents a comprehensive exploration of short-term stock market trend prediction using state-of-the-art machine learning techniques, anchored in a decade-long analysis of the S&P 500 Index. Leveraging a rich dataset spanning ten years, our study investigates the performance of various machine learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector machines (SVM), Random Forest, and Gated Recurrent Unit (GRU) models. These models were evaluated based on their ability to harness financial indicators and trading volume data, providing a deep dive into the complex factors influencing stock market movements. The GRU model, in particular, demonstrated exceptional performance, underscoring its potential as a superior predictive tool due to its efficient handling of temporal dependencies. This comparative analysis not only highlights the importance of selecting the appropriate model to match specific data characteristics but also contributes significantly to the broader understanding of how machine learning can enhance the prediction of stock market dynamics. © 2024 IEEE.

关键词： Long short-term memory

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TSC prediction and dynamic control of BOF steelmaking with state-of-the-art machine learning and deep learning methods

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Journal of Iron and Steel Research international 2024年第1期31卷 174-194页

作者： Tian-yi Xie Cai-dong Zhang Quan-lin Zhou Zhi-qiang Tian Shuai Liu Han-jie Guo Material Technology Research Institute Hesteel GroupShijiazhuang050023HebeiChina Tangsteel Company Hesteel GroupTangshan063611HebeiChina School of Artificial Intelligence Beijing Technology and Business UniversityBeijing100048China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China

Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC *** maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the *** number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as ***,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time *** optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,*** field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carri

关键词： BOF steelmaking In-blow prediction TSC test machine learning Deep learning Field application

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An Effective machine learning Algorithm for Forecasting the Market Value of a House 3

An Effective Machine Learning Algorithm for Forecasting the ...

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3rd international conference on applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Ancy, S.G. Praveenchandar, J. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350375190

machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy. © 2024 IEEE.

关键词： Forecasting

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Application Of machine learning Agriculture Between Developing And Developed Countries: A Systematic Literature Review

Application Of Machine Learning Agriculture Between Developi...

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2024 international conference Automatics and Informatics, ICAI 2024

作者： Rafif, Muhammad Resnandya Leonardi, Mario Putra, Nicholaus Gunawan, Ali Bina Nusantara University Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350353907

The use of machine learning technology in the agricultural industry has grown rapidly in recent decades, especially in the 1990s-2000s which became important with the emergence of information and communication technology. Some of these things allow various countries that have sophisticated technological resources such as developed countries to develop machine learning technology and Internet of Things components for the agricultural industry. The use of machine learning in the agricultural industry opens up new opportunities for data-intensive science because the amount of agricultural data collected is increasing. In addition, algorithms in machine learning can also be applied to further improve the intelligence and functionality of the application. The purpose of this literature review is to investigate the capabilities and impacts of using machine learning in the Agricultural industry as well as the influence of the use and benefits received from the application of Agricultural machine learning for developing countries. © 2024 IEEE.

关键词： Agricultural economics

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Forecasting Pupils Performance through machine learning Approaches 5th

Forecasting Pupils Performance through Machine Learning Appr...

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5th international conference on data science, machine learning and Applications

作者： Raj, Amara Rithik Regulwar, Ganesh B. Anvitha, Rangineni Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Student performance vaticination plays a pivotal part in relating and addressing academic challenges early [3], enabling targeted interventions and personalized support. This study aims to improve academic outcomes by accurately predicting student performance. To achieve this thing, varied machine learning algorithms were employed to develop dependable models [2] based on student-related attributes such as demographic information, socio-economic background, past academic records, and engagement factors. The models were trained on a portion of the data set and evaluated using appropriate metrics to measure predictive accuracy. The results demonstrate that the machine learning algorithms were effective in predicting student performance [5], with varying levels of accuracy. This information enables educators to identify and support students who may require additional resources. By employing machine learning algorithms, educational stakeholders canmake informed decisions and allocate resources effectively to improve student outcomes.

关键词： Student performance prediction machine learning Gaussian Naive Bayes Gradient Boosting Random Forest Academic performance

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